Because individuals who are more connected within their social network are more likely to come into contact with any given individual in that network, the logic goes that these social butterflies are also more likely to be the first to catch whatever communicable bug is going around.

"People who are very active socially and spend time with other people will have a higher risk of contagion. It's kind of intuitive," says Dr. Christopher Ohl, associate professor of Infectious Diseases, Wake Forest University Baptist Medical Center.

"Here we are focused on early detection. Think back to last fall and we had to make hard choices about who had to get vaccinated first. If you could have these "friend" sensors and know which part of the country were getting the flu earlier, it would give you more time to get vaccines there," says co-author James Fowler, a professor specializing in social networks at the University of California, San Diego.

While tracking high-risk individuals in a population in order to gauge the spread of the disease is not a new concept, public health experts say Fowler's approach is novel, and potentially useful.

"There is a tradition of defining sentinel populations for public health -- 'canaries in the coal mine' based on risk factors," says Stephen Eubank, professor of Virginia Bioinformatics Institute at Virginia Tech. This research applies this practice to theories about how to identify people who are socially central in a population, he says.

Charting Social Dominance, Following the Flu

To identify the students who were at the center of the student body social network, researchers played off of something called the friend paradox.

Researchers asked a random sample of students to name a few of their friends and then enlisted as many of these friends as possible into the study as members of the "friend" group. Any person in the "random sample" group who was named by another as a friend was bumped into the "friend" group.

"It's an old idea in social network analysis: if you randomly chose someone in the population and asked them how many friends they have and then talked to one of their friends. That friend, on average, will have more friends than the original person you asked. In short, your friends have more friends than you do," Fowler says.

Researchers also performed traditional mapping of social dominance of the sample and verified that the "friend" group represented the more socially central students.

The two groups were monitored for symptoms of flu both by bi-weekly self report and visits made to the university health services. About 8 percent of the total sample was diagnosed with the flu by health services and 32 percent were displaying flu-like symptoms based on self-report. The only difference between the two groups was that the "friends" group began getting the flu and had a peak of flu incidence 13.9 days before the others.

"The whole epidemic curve is shifted forward in time by two weeks. This group is like looking into a crystal ball," Fowler says.

This is the first time this "friend sensor" method for predicting disease spread has been tested in this way, but Fowler and co-author Nicholas Christakis, of Harvard Medical School, feel that the tool has the potential to be applied nationally during epidemics or for charting changes in non-biological factors such as drug and alcohol use or obesity.

From Harvard to the World at Large?

The two biggest limitations of this study are its novelty and its study population. Because it is the first study of its kind, it's too early to know whether the robust results seen would be true in other cases of disease or behavior contagion.

The fact that subjects are students who are part of a particularly socially integrated environment -- the college campus, also calls into question how applicable this approach would be in the population at large who does not, for example, share a dining hall.

The same method that works on a social college campus most likely will not apply to a larger population, says Dr. Pascal James Imperato, dean and distinguished service professor at the SUNY Downstate School of Public Health.

"In a large city center, for instance, your population could be defined by the use of public transportation, and that setting could provide greater exposure to disease than kids on the Harvard campus interacting with a set friend group," he says.

The hypochondriac effect may have also exaggerated how predictive the friend group was, says Ed Hsu, associate professor of Public Health Informatics at the University of Texas. Those in the friend group are more likely to know someone who has the flu and hence may be more likely to think (and report) that they are coming down with it themselves.

Given its limitations, could there be a place for this friend metric in public health monitoring?

Experts were divided, but many thought it might have special applications in smaller social networks.

Given public health departments already monitor high risk groups, such as school children and health care workers as a means of predicting outbreaks, Imperato says that using the friend metric for tracking flu in the general population would be redundant.

Ohl agrees, but notes that for emerging infections or more unusual or behavioral (such as drug use) outbreaks, the "friend sensor" could provide a valuable tracking tool.